Enhanced MapReduce Performance for the Distributed Parallel Computing: Application of the Big Data

Milhem, Nathier; Abualigah, Laith; Nadimi-Shahraki, Mohammad H.; Jia, Heming; Ezugwu, Absalom E.; Hussien, Abdelazim G.

doi:10.1007/978-3-031-17576-3_8

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…In his article, the author has implemented the MapReduceApriori (MRA) algorithm on the Apache Hadoop cluster. This algorithm uses two distinct functions, namely Map and Reduce, with the aim of detecting repeated sets of k-elements [6].…”

Section: Mapreduce Paradigmmentioning

confidence: 99%

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Load Balancing Large Data Sets in a Hadoop Cluster

Anselme A,

Alain N,

Francis

et al. 2023

IJDPS

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With the interconnection of one to many computers online, the data shared by users is multiplying daily. As a result, the amount of data to be processed by dedicated servers rises very quickly. However, the instantaneous increase in the volume of data to be processed by the server comes up against latency during processing. This requires a model to manage the distribution of tasks across several machines. This article presents a study of load balancing for large data sets on a cluster of Hadoop nodes. In this paper, we use Mapreduce to implement parallel programming and Yarn to monitor task execution and submission in a node cluster.

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Section: Mapreduce Paradigmmentioning

confidence: 99%

“…The results of experiments carried out by NathierMilhem et al show that HDFS with a distributed cache mechanism offers superior learning performance compared with conventional HDFS, which has no cache, and with HDFS with a centralized cache mechanism [6].…”

Section: Reading and Writing A File In Hdfsmentioning

confidence: 99%

Load Balancing Large Data Sets in a Hadoop Cluster

Anselme A,

Alain N,

Francis

et al. 2023

IJDPS

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“…Utility values go beyond traditional support and frequency measures by incorporating profits or costs associated with item sets. This mining technique finds applications in marketing, bioinformatics, finance, and other domains [Milhem et al 2022]. The UP-Tree, a data structure that efficiently stores high-utility item sets, is commonly used in various high utility pattern mining algorithms [Hassan et al 2022].…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data

Ghaib,

Alsalhi,

Hayder

et al. 2023

JCSTS

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High utility pattern mining is an analytical approach used to identify sets of items that exceed a specific threshold of utility values. Unlike traditional frequency-based analysis, this method considers user-specific constraints like the number of units and benefits. In recent years, the importance of making informed decisions based on utility patterns has grown significantly. While several utility-based frequent pattern extraction techniques have been proposed, they often face limitations in handling large datasets. To address this challenge, we propose an optimized method called improving the efficiency of Distributed Utility itemsets mining in relation to big data (IDUIM). This technique improves upon the Distributed Utility item sets Mining (DUIM) algorithm by incorporating various refinements. IDUIM effectively mines item sets of big datasets and provides useful insights as the basis for information management and nearly real-time decision-making systems. According to experimental investigation, the method is being compared to IDUIM and other state algorithms like DUIM, PHUI-Miner, and EFIM-Par. The results demonstrate the IDUIM algorithm is more efficient and performs better than different cutting-edge algorithms.

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Analysis of the feasibility and advantages of using big data technology for English translation

2023

Soft Comput

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Enhanced MapReduce Performance for the Distributed Parallel Computing: Application of the Big Data

Cited by 9 publications

References 31 publications

Load Balancing Large Data Sets in a Hadoop Cluster

Load Balancing Large Data Sets in a Hadoop Cluster

Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data

Analysis of the feasibility and advantages of using big data technology for English translation

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